Study on system dynamics of evolutionary mix-game models

Gou, Chengling; Guo, Xiaoqian; Chen, Fang

doi:10.1016/j.physa.2008.07.023

Cited by 1 publication

(2 citation statements)

References 16 publications

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“…According to a widely accepted assumption that excess demand exerts an influence on the price of the 110514-2 asset, and the change of price is proportional to the excess demand in a financial market, [5] the time series of price of the asset P [(t)] can be calculated based on the time series of excess demand as…”

Section: D[(t)]mentioning

confidence: 99%

“…In real financial markets, participants can learn from others and change their strategies or their investment behaviours according to the situations of markets. Gou et al [5] have studied the system dynamics of evolutionary mix-game models in which agents can change their strategies during the game. In the present paper, we investigate such a situation that some participants in real markets can change their investment behaviours by assigning the evolutionary abilities to agents: if the winning rates of agents are smaller than a threshold, they will join the other group; and agents will repeat such an evolution at certain time intervals.…”

Section: Introductionmentioning

confidence: 99%

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System dynamics of behaviour-evolutionary mix-game models

2010

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In real financial markets there are two kinds of traders: one is fundamentalist, and the other is a trend-follower. The mix-game model is proposed to mimic such phenomena. In a mix-game model there are two groups of agents: Group 1 plays the majority game and Group 2 plays the minority game. In this paper, we investigate such a case that some traders in real financial markets could change their investment behaviours by assigning the evolutionary abilities to agents: if the winning rates of agents are smaller than a threshold, they will join the other group; and agents will repeat such an evolution at certain time intervals. Through the simulations, we obtain the following findings: (i) the volatilities of systems increase with the increase of the number of agents in Group 1 and the times of behavioural changes of all agents; (ii) the performances of agents in both groups and the stabilities of systems become better if all agents take more time to observe their new investment behaviours; (iii) there are two-phase zones of market and non-market and two-phase zones of evolution and non-evolution; (iv) parameter configurations located within the cross areas between the zones of markets and the zones of evolution are suited for simulating the financial markets.

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Section: D[(t)]mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%